The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Matching
We present a new bio-inspired approach applied to a problem of stereo images matching. This approach is based on an artifical epidemic process, that we call “the infection algorithm.” The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D informations which allow the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to only produce the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, that propagate like an artificial epidemy over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.
KeywordsCellular Automaton Cellular Automaton Transition Rule Stereo Image Stereo Match
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- 1.Abbey, H.: An Examination of the Reed Frost Theory of Epidemics. Human Biology 24, 201–233 (1952)Google Scholar
- 8.Maniatty, W., Szymanski, B., Caraco, T.: Parallel Computing with Generalized Cellular Automata. Nova Science Publishers, Inc., Bombay (2001)Google Scholar
- 9.Maniatty, W., Szymanski, B.K., Caraco, T.: Epidemics Modeling and Simulation on a Parallel Machine. In: IASTED, editor Proceedings of the International Conference on Applied Modeling and Simulation, Vancouver, Canada, pp. 69–70 (1993)Google Scholar
- 11.Olague, G.: Automated Photogrammetric Network Design using Genetic Algorithms. Photogrammetric Engineering & Remote Sensing 68(5), 423–431 (2002)Google Scholar
- 12.Olague, G., Hernández, B., Dunn, E.: Accurate L-Corner Measurement using USEF Functions and Evolutionary Algorithms. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 410–421. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 13.Olague, G., Hernández, B.: A New Accurate and Flexible Model Based Multi-corner Detector for Measurement and Recognition. Pattern Recognition Letters (to appear)Google Scholar
- 15.Sipper, M.: Evolution of Parallel Cellular Machines. Springer, Heidelberg (1997)Google Scholar
- 17.Watts, D.J.: Small Worlds. Princeton University Press, Princeton (1999)Google Scholar